Quickest detection of nuclear radiation using a sensor network

Qian, Lijun; Fuller, John; Chang, Ing

doi:10.1109/ths.2012.6459925

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…Fusion of correlated decisions is another path to address this issue. Local decision from multiples sensors are sending to a fusion center and correlation test is done (CUMSUM test or test based on copula theory) [12], [13].…”

Section: Concept Of the Correlation Approachmentioning

confidence: 99%

Moving Sources Detection Algorithm for Radiation Portal Monitors Used in a Linear Network

Coulon

Kondrasovs

Boudergui

et al. 2014

IEEE Trans. Nucl. Sci.

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To monitor radioactivity passing through a vehicle such as a pedestrian, a car, a train or a truck, Radiation Portal Monitors (RMP) are commonly employed. These detection systems consist of a large volume detector set close to the potential source path. An alarm is then triggered when the signal rises over a threshold initially estimated in view of the natural background signal. The approach developed in this work makes use of several detectors in a network along the source path. The correlation detection approach is elaborated to take into account the temporal periodicity of the signals taken by all distributed sensors as a whole. This new detection method is then not based only on counting statistics but also on the temporal series analysis. Therefore, a specific algorithm has been developed in our laboratory for this security application and shows a significant improvement, especially in terms of detection probability increase and false alarm reduction. This paper presents the theoretical approach and promising results obtained by simulation.

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Section: Concept Of the Correlation Approachmentioning

confidence: 99%

Moving Sources Detection Algorithm for Radiation Portal Monitors Used in a Linear Network

Coulon

Kondrasovs

Boudergui

et al. 2014

IEEE Trans. Nucl. Sci.

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“…Next, each grid is rescored according to the number of included boundary grids if a sensor is placed in it. As the grid (4, 3) in the Figure 2(b), it includes the boundary grids (1,2), (1,3), (1,4), (1,5), and (1,6). So, its score is 5 (note that a grid is counted as included if more than two-thirds of the grids are covered by the sensor).…”

Section: Letmentioning

confidence: 99%

“…Usually, sensors have the wireless communication components to deliver their collected data back to the sink , which is a data collector in the network. Sensors can be deployed in the field for the environmental habitat monitoring [1], the natural hazards monitoring [2], the forest-fire detection [3], or the nuclear radiation detection [4]. Sensors are also used in the medical center to quicken the emergency response [5, 6].…”

Section: Introductionmentioning

confidence: 99%

A Jigsaw-Based Sensor Placement Algorithm for Wireless Sensor Networks

Huang

Chang

2013

International Journal of Distributed Sensor Networks

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Current deterministic sensor deploying methods always include the uncovered space greedily to reduce the number of deployed sensors. Because the sensing area of each sensor is circle-like, these greedily methods often divide the region of interest to multiple tiny and scattered regions. Therefore, many additional sensors are deployed to cover these scattered regions. This paper proposes a Jigsaw-based sensor placement (JSP) algorithm for deploying sensors deterministically. Sensors are placed at the periphery of the region of interest to prevent separating the region of interest to isolated regions. An enhanced mechanism is also proposed to improve the time complexity of the proposed method. The scenarios with and without obstacles are evaluated. The simulation results show that the proposed method can cover the whole region of interest with fewer deployed sensors. The effective coverage ratio of JSP method is less than 2. It is better than the maximum coverage method and the Delaunay triangulation method. The deploying sensors have more efficient coverage area, and the distribution of the incremental covered area is close to normal distribution.

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“…A different approach is to form a fixed sensor network on a grid of monitoring points with a large number of relatively inexpensive (hence low accuracy) sensors. There have been studies using such a fixed network for detection and monitoring of nuclear material, including mobile sources. Small‐scale experiments have been conducted to test such systems.…”

Section: Introductionmentioning

confidence: 99%

A dynamic fusion system for fast nuclear source detection and localization with mobile sensor networks

Grelaud

Mitra

Xie

et al. 2017

Appl Stoch Models Bus & Ind

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This paper proposes a dynamic system, with an associated fusion learning inference procedure, to perform real-time detection and localization of nuclear sources using a network of mobile sensors. This is motivated by the need for a reliable detection system in order to prevent nuclear attacks in major cities such as New York City. The approach advocated here installs a large number of relatively inexpensive (and perhaps relatively less accurate) nuclear source detection sensors and GPS devices in taxis and police vehicles moving in the city. Sensor readings and GPS information are sent to a control center at a high frequency, where the information is immediately processed and fused with the earlier signals. We develop a real-time detection and localization method aimed at detecting the presence of a nuclear source and estimating its location and power. We adopt a Bayesian framework to perform the fusion learning and use a sequential Monte Carlo algorithm to estimate the parameters of the model and to perform real-time localization. A simulation study is provided to assess the performance of the method for both stationary and moving sources. The results provide guidance and recommendations for an actual implementation of such a surveillance system.Appl. Stochastic Models Bus. Ind. 2018, 34 4-19 GRELAUD ET AL. t ), of all the sensors in the system and the binary signals the sensors send to the control center t = (d (1) t , · · · , d (n t ) t ). Here, n t is the number of active sensors. The locations, t , are assumed to be accurate, but the signals received by the control center could possibly be false.This model assumes the presence of one source. It is compared with the baseline model, where no source is present, for the purpose of detecting the presence of a source. Details of the comparison are presented in Section 3.2. The extension to multiple source detection is discussed in Section 6.State-space models have been widely used in many applications, including source tracking [29,30,[36][37][38][39][40][41][42][43]. However, our approach is different from standard source tracking problems, mainly because in our case, both negative and positive binary signals are observed and used. The use of state-space formation allows the data collected at time t to be used for the estimation of the location of the source x t with the information observed in the past, through an updating of the previous estimation of x t−1 , instead of performing an independent analysis at each time point. A source's motion possesses 6

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Quickest detection of nuclear radiation using a sensor network

Cited by 5 publications

References 19 publications

Moving Sources Detection Algorithm for Radiation Portal Monitors Used in a Linear Network

Moving Sources Detection Algorithm for Radiation Portal Monitors Used in a Linear Network

A Jigsaw-Based Sensor Placement Algorithm for Wireless Sensor Networks

A dynamic fusion system for fast nuclear source detection and localization with mobile sensor networks

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